Modelling, forecasting and trading with a new sliding window approach: the crack spread example
| Autor: | Christian L. Dunis, Samer Khalil, Andreas Karathanasopoulos |
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| Rok vydání: | 2016 |
| Předmět: |
Mathematical optimization
050208 finance Artificial neural network Crack spread Computer science Autoregressive conditional heteroskedasticity 05 social sciences 02 engineering and technology Autoregressive model Sliding window protocol Multilayer perceptron 0502 economics and business 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Autoregressive–moving-average model General Economics Econometrics and Finance Finance Spread trade |
| Zdroj: | Quantitative Finance. 16:1875-1886 |
| ISSN: | 1469-7696 1469-7688 |
| Popis: | The scope of this analysis is the modeling and the tracking of the crack spread with a sophisticated new non-linear approach. The selected trading period covers 2087 trading days starting on 09/05/2005 and ending on 21/12/2015. The proposed model is a combined particle swarm optimiser (PSO) and a radial basis function (RBF) neural network which is trained using sliding windows of 300 and 400 days. This is benchmarked against a multilayer perceptron (MLP) neural network and higher order neural network using the same data-set. Outputs from the neural networks provide forecasts for 5 days ahead trading simulations. To model the spread an expansive universe of 250 inputs across different asset classes is also used. Included in the input data-set are 20 Autoregressive Moving Average models and 10 Generalized Autoregressive Conditional Heteroscedasticity volatility models. Results reveal that the sliding window approach to modelling the crack spread is effective when using 300 and 400 days training periods. Sli... |
| Databáze: | OpenAIRE |
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